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3D-QSAR of p38-α mitogen-activated protein kinase inhibitors: pyridopyridazin-6-ones (part 1)

Authors Bhansali SG, Kulkarni VM

Received 29 June 2013

Accepted for publication 15 August 2013

Published 18 October 2013 Volume 2013:3 Pages 29—41


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Video abstract presented by Bhansali SG and Kulkarni VM.

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Sujit G Bhansali, Vithal M Kulkarni

Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth Deemed University, Pune, Maharashtra, India

Abstract: p38-α mitogen-activated protein kinase (MAPK) is considered to be a novel target for the development of new anti-inflammatory agents. Inhibitors of this enzyme can provide new therapeutics for the treatment of various inflammatory diseases, such as rheumatoid arthritis, Crohn’s disease, and inflammatory bowel disease. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for pyridopyridazin-6-ones exhibiting p38-α MAPK inhibition were performed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The study included 70 compounds. The QSAR model was generated using a training set of 45 compounds. The energy-minimized structure of the most active compound in the series, compound 70, was used as a template for alignment, which was done with an aligned database. The optimum partial least squares analysis model on CoMFA and CoMSIA descriptors showed “leave-one-out” cross-validation correlation coefficients (q2) of 0.611 and 0.493, and non-cross-validated correlation coefficients (r2ncv) of 0.973 and 0.815, respectively. The statistical quality of the generated model was further analyzed by bootstrapping analysis and by more robust cross-validation testing using cross-validation by two groups (leave-half-out method) to check the internal reliability within the dataset. The predictive ability of the generated CoMFA and CoMSIA model was analyzed by an external test set of 25 compounds, resulting in predictive correlation coefficients (r2pred) of 0.630 and 0.403, respectively. The generated model may provide useful guidance for future synthesis of potent p38-α MAPK inhibitors.

Keywords: three-dimensional quantitative structure-activity relationship, comparative molecular field analysis, comparative molecular similarity indices analysis, p38-α MAPK

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